Most organizations, in particular large organizations with a significant client/consumer base, are constantly faced with the problem of maximizing the value they can extract from their clients/consumers while minimizing the cost in doing so. Preferably, the organization would like to institute one or more programs that are both effective at targeting the clients' needs while efficiently applying a limited amount of resources (e.g., money, time, and effort). However, typical client bases are made up of disparate individuals/entities that have widely varying behaviors, attitudes, and needs and may further have varying propensities for obtaining new products and/or services from the organization.
Typically, most prior art systems and methods address the issue of how to model the needs/desires of their client/consumer base from a product-oriented approach rather than a client-oriented approach. For example, traditional sales models focus on selling a particular product and then determine which clients, or an approximation of a “typical client”, are most likely to purchase that particular product. Such an approach directs interactions with the clients towards a sales call or marketing effort that is focused on the particular product first and glosses over the attitudes, desires, and needs of the clients. These prior art approaches typically results in relatively low sales rates (e.g., 2% direct mail response) since there is no real effort made by the organization to determine what the client wants.
Some prior art approaches may attempt to take the client's needs into account, but still these approaches retain the focus on what the organization wants, i.e., the product to be sold. One reason for this is that even the most robust client and product database has limited information about the client and this dearth of client information makes it difficult, if not impossible, to distinguish clients who may appear to be similar but in actuality have widely varying attitudes, behaviors, and needs.
At best, prior art systems and methods cannot achieve the necessary granularity of analysis in order to direct how the organization should interact with the various needs and concerns of its clients. Consequently, prior art systems and methods may only target a vaguely-determined “median person” or “median behavior” or “the average Joe” or some other gross characterization of the client base. In some instances, prior art systems and methods may target a very limited number of groups, e.g., “the average Joe” and “the average Jane”, and perhaps “the average Jim”. Such prior art systems and methods operate under the assumption that “the average Joe/Jane/Jim” is a sufficient indicator of how most of the client base will act and the organization will conduct itself accordingly when interacting with each client/consumer in the client base. Obviously, lumping everyone in one basket in a “one size fits all” approach or even into a few baskets is not an effective means of dealing with a client/consumer base made up of distinct, and perhaps contrasting, individuals and behaviors.
For example, prior art systems and methods which direct how an organization should interact with its clients using “the average Joe” approach are most likely to determine a single sales strategy and/or marketing strategy and/or cross-selling strategy, and/or consumer retention strategy and apply that single strategy to every consumer regardless of whether a particular consumer is even interested in, e.g., new sales, or a cross-sell product or service. Additionally, “the average Joe” approach may result in a uniformly-applied evaluation of an attrition probability of a product held by a consumer, or may uniformly include or exclude a consumer from a future product offering which may completely mismanage a consumer's needs and therefore may actually push the consumer out the door and into the arms of a waiting competitor. Furthermore, “the average Joe” approach is wholly inadequate for hierarchically ranking the consumers, and/or determining a clustering of the consumers.
The present disclosure provides for novel systems and computerized methods to be used by an organization (including, without limitation, a bank or financial institution) to overcome the above-described deficiencies in the prior art. Embodiments of these methods include, for example, viewing the clients in the database against all of the relevant products/services thereby capturing a more complete understanding of the client/product environment allowing for directing improved interactions with the clients. In certain embodiments, there are five main points that may be taken into account: (1) each client may be viewed from two perspectives, (a) the current product mix of products/services that the client has/uses, and (b) one or more potential future product mix of products/services; (2) each of the product mixes may be assigned a value; (3) a difference between a current and a potential future product mix may lead to multiple product recommendations for a client versus the traditional single product recommendation based on prior art models; (4) a matrix of current values versus a potential values may be determined and analyzed to thereby direct future interactions with the client(s); and (5) a client's movement over time through the matrix may be tracked to thereby determine patterns applicable to that client. Embodiments described herein may be applied to a cluster or segment of clients who are sufficiently alike to one another and dissimilar to other clients/clusters/segments.
Accordingly, it is an object of the present disclosure to provide a method for directing an interaction with at least a first consumer and/or evaluating a consumer database, where the method may include providing a computer database comprising first information about plural consumers and second information about predetermined products, wherein the plural consumers include the first consumer, and wherein each of the plural consumers is associated with a current product mix comprising certain ones of the predetermined products independent of an association of another consumer with the predetermined products. Additionally, the method may calculate, using a computer processor, individually for each one of the plural consumers, (i) an aggregate first Residual Life Time Value (“RLTV”) estimate from the time variable products in the current product mix for said one consumer; (ii) an aggregate second RLTV estimate from the finite duration products in the current product mix for said one consumer; (iii) an aggregate third RLTV estimate from the aggregate first RLTV estimate and from the aggregate second RLTV estimate; and (iv) an aggregate PLTV estimate from preselected products not in the current product mix for said one consumer. Furthermore, the method may: calculate, using a processor, the likelihood of the first consumer to acquire one or more of the predetermined products not in the current product mix for the first consumer; analyze a distribution of the aggregate third RLTV estimates for the plural consumers; analyze a distribution of the aggregate PLTV estimates for the plural consumers; evaluate the first consumer as a function of the distribution of the third aggregate RLTV estimates and as a function of the distribution of the aggregate PLTV estimates; and interact with the first consumer based on said evaluation of the first consumer.
Additionally, the above method may further include stratifying the database into plural segments according to a predetermined criteria, and wherein each of the plural consumers may be assigned to one of the plural segments according to the predetermined criteria.
Further, the above method may include determining a matrix of values from the distribution of the aggregate third RLTV estimates for the plural consumers and from the distribution of the aggregate PLTV estimates for the plural consumers; wherein the matrix may have N number of rows encompassing a first range of quantities for the distribution of the aggregate third RLTV estimates and may have M number of columns encompassing a second range of quantities for the distribution of the aggregate PLTV estimates thereby creating a matrix of X cells where X=N*M (where M may be greater than, less than, or equal to N).
Still further, the above method may assign the first consumer to one of the X cells based at least in part on the evaluation of the first consumer.
Yet further, the above method may determine an interaction with the first consumer based at least in part on the cell assignment.
Even further, the above method may assign the first consumer to one of the X cells based at least in part on a recalculated aggregate third RLTV estimate and a recalculated aggregate PLTV estimate.
Even still further, the above method may determine the interaction with the first consumer based at least in part on a difference between the cell assignment of the first consumer based at least in part on the aggregate third RLTV estimate for the first consumer and the aggregate PLTV estimate for the first consumer and the cell assignment of the first consumer based at least in part on the recalculated aggregate third RLTV estimate and the recalculated aggregate PLTV estimate.
It is another object of the present disclosure to provide a system for evaluating a first consumer, including: a computer database comprising first information about plural consumers and second information about predetermined products, wherein the plural consumers include the first consumer, and wherein each of the plural consumers is associated with a current product mix comprising certain ones of the predetermined products independent of an association of another consumer with the predetermined products; a computer processor; and a computer readable storage medium comprising computer-executable instructions stored thereon, said instructions when executed causing said processor to: (1) individually for each one of the plural consumers: (i) calculate an aggregate first Residual Life Time Value (“RLTV”) estimate from the time variable products in the current product mix for said one consumer, (ii) calculate an aggregate second RLTV estimate from the finite duration products in the current product mix for said one consumer, (iii) calculate an aggregate third RLTV estimate from the aggregate first RLTV estimate and from the aggregate second RLTV estimate, and (iv) calculate an aggregate PLTV estimate from preselected products not in the current product mix for said one consumer; and to (2) calculate the likelihood of the first consumer to acquire one or more of the predetermined products not in the current product mix for the first consumer; (3) analyze a distribution of the aggregate third RLTV estimates for the plural consumers; (4) analyze a distribution of the aggregate PLTV estimates for the plural consumers; and (5) evaluate the first consumer as a function of the distribution of the third aggregate RLTV estimates and as a function of the distribution of the aggregate PLTV estimates.
It is yet another object of the present disclosure to provide a method for directing an interaction with at least a first consumer, the method may include providing a computer database which contains first information about plural consumers and second information about predetermined products, wherein the plural consumers include the first consumer, and wherein each of the plural consumers is associated with a current product mix comprising certain ones of the predetermined products independent of an association of another consumer with the predetermined products; and for a time variable product in the current product mix for a one of the plural consumers: (i) determining a baseline product survival curve, (ii) determining a shift in the baseline product survival curve as a function of characteristics of said one consumer to thereby determine a consumer product survival curve, (iii) calculating, using a processor, an area under the consumer product survival curve, (iv) calculating, using a processor, an estimated potential residual profit from the calculated area, (v) determining a first Residual Life Time Value (“RLTV”) estimate for said time variable product for said one consumer from the calculated estimated potential residual profit, (vi) repeating (i) through (v) for each time variable product in the current product mix for said one consumer, and (vii) determining an aggregate first RLTV estimate for said one consumer from the first RLTV estimate for each said time variable product for said one consumer; the foregoing may be repeated for each one of the plural consumers.
Additionally, the method may include, for a finite duration product in the current product mix for a one of the plural consumers: (i) determining a remaining outstanding balance (as an example, since this may apply to any monetary amount such as purchases made during a time period, or a price), (ii) multiplying, using a processor, the remaining outstanding balance by a funds transfer pricing value for said finite duration product to thereby determine an approximate residual value, (iii) determining a second RLTV estimate for said finite duration product for said one consumer from the approximate residual value, (iv) repeating (i) through (iii) for each finite duration product in the current product mix for said one consumer, and (v) determining an aggregate second RLTV estimate for said one consumer from the second RLTV estimate for each said finite duration product for said one consumer; the foregoing may be repeated for each one of the plural consumers.
Further, the method may include, individually for each of the plural consumers, determining an aggregate third RLTV estimate from that consumer's aggregate first RLTV estimate and from that consumer's aggregate second RLTV estimate; and calculating, using a processor, the likelihood of the first consumer to acquire one or more of the predetermined products not in the current product mix for the first consumer.
Still further, the method may include, for a preselected product not in the current product mix of a one of the plural consumers: (i) determining a baseline product survival curve, (ii) calculating, using a processor, an area under the baseline product survival curve, (iii) calculating, using a processor, an estimated potential residual profit from the calculated area, (iv) determining a Potential Life Time Value (“PLTV”) estimate for said preselected product for said one consumer from the calculated area, (v) repeating (i) through (iv) for each preselected product not in the current product mix for said one consumer, and (vi) determining an aggregate PLTV estimate for said one consumer from the PLTV estimate for each said preselected product for said one consumer; the foregoing may be repeated for each one of the plural consumers.
Yet further, the method may include analyzing a distribution of the aggregate third RLTV estimates for the plural consumers; analyzing a distribution of the aggregate PLTV estimates for the plural consumers; evaluating the first consumer as a function of the distribution of the third aggregate RLTV estimates and as a function of the distribution of the aggregate PLTV estimates; and interacting with the first consumer based on said evaluation of the first consumer.
The above advantages, as well as many other advantages, of the present disclosure will be readily apparent to one skilled in the art to which the disclosure pertains from a perusal of the claims, the appended drawings, and the following detailed description.